Overview

Dataset statistics

Number of variables24
Number of observations14993
Missing cells1269
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory192.0 B

Variable types

Categorical13
Numeric11

Alerts

Name has a high cardinality: 9060 distinct values High cardinality
RescuerID has a high cardinality: 5595 distinct values High cardinality
Description has a high cardinality: 14032 distinct values High cardinality
PetID has a high cardinality: 14993 distinct values High cardinality
Gender is highly correlated with QuantityHigh correlation
Vaccinated is highly correlated with DewormedHigh correlation
Dewormed is highly correlated with VaccinatedHigh correlation
Quantity is highly correlated with GenderHigh correlation
Vaccinated is highly correlated with DewormedHigh correlation
Dewormed is highly correlated with VaccinatedHigh correlation
Vaccinated is highly correlated with DewormedHigh correlation
Dewormed is highly correlated with VaccinatedHigh correlation
Vaccinated is highly correlated with DewormedHigh correlation
Dewormed is highly correlated with VaccinatedHigh correlation
Type is highly correlated with Breed1 and 1 other fieldsHigh correlation
Breed1 is highly correlated with TypeHigh correlation
Breed2 is highly correlated with TypeHigh correlation
Gender is highly correlated with QuantityHigh correlation
Color1 is highly correlated with Color2High correlation
Color2 is highly correlated with Color1 and 1 other fieldsHigh correlation
Color3 is highly correlated with Color2High correlation
Vaccinated is highly correlated with Dewormed and 1 other fieldsHigh correlation
Dewormed is highly correlated with Vaccinated and 1 other fieldsHigh correlation
Sterilized is highly correlated with Vaccinated and 1 other fieldsHigh correlation
Quantity is highly correlated with GenderHigh correlation
Name has 1257 (8.4%) missing values Missing
PetID is uniformly distributed Uniform
PetID has unique values Unique
Age has 179 (1.2%) zeros Zeros
Breed2 has 10762 (71.8%) zeros Zeros
Color2 has 4471 (29.8%) zeros Zeros
Color3 has 10604 (70.7%) zeros Zeros
Fee has 12663 (84.5%) zeros Zeros
VideoAmt has 14419 (96.2%) zeros Zeros
PhotoAmt has 341 (2.3%) zeros Zeros

Reproduction

Analysis started2022-01-24 19:50:40.575297
Analysis finished2022-01-24 19:51:00.826215
Duration20.25 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
8132 
2
6861 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18132
54.2%
26861
45.8%

Length

2022-01-24T20:51:00.902233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:01.142290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
18132
54.2%
26861
45.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Name
Categorical

HIGH CARDINALITY
MISSING

Distinct9060
Distinct (%)66.0%
Missing1257
Missing (%)8.4%
Memory size117.3 KiB
Baby
 
66
Lucky
 
64
No Name
 
54
Brownie
 
54
Mimi
 
52
Other values (9055)
13446 

Length

Max length47
Median length6
Mean length9.510993011
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7808 ?
Unique (%)56.8%

Sample

1st rowNibble
2nd rowNo Name Yet
3rd rowBrisco
4th rowMiko
5th rowHunter

Common Values

ValueCountFrequency (%)
Baby66
 
0.4%
Lucky64
 
0.4%
No Name54
 
0.4%
Brownie54
 
0.4%
Mimi52
 
0.3%
Blackie49
 
0.3%
Puppy45
 
0.3%
Kittens39
 
0.3%
Max39
 
0.3%
Kitty39
 
0.3%
Other values (9050)13235
88.3%
(Missing)1257
 
8.4%

Length

2022-01-24T20:51:01.218316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1062
 
4.3%
and437
 
1.8%
kittens318
 
1.3%
puppies298
 
1.2%
kitten274
 
1.1%
for259
 
1.0%
adoption214
 
0.9%
puppy206
 
0.8%
boy193
 
0.8%
2184
 
0.7%
Other values (6908)21409
86.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

ZEROS

Distinct106
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.45207764
Minimum0
Maximum255
Zeros179
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:01.324338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q312
95-th percentile48
Maximum255
Range255
Interquartile range (IQR)10

Descriptive statistics

Standard deviation18.15579041
Coefficient of variation (CV)1.737050856
Kurtosis20.77213827
Mean10.45207764
Median Absolute Deviation (MAD)2
Skewness3.762974928
Sum156708
Variance329.6327253
MonotonicityNot monotonic
2022-01-24T20:51:01.434366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23503
23.4%
12304
15.4%
31966
13.1%
41109
 
7.4%
12967
 
6.4%
24651
 
4.3%
5595
 
4.0%
6558
 
3.7%
36417
 
2.8%
8309
 
2.1%
Other values (96)2614
17.4%
ValueCountFrequency (%)
0179
 
1.2%
12304
15.4%
23503
23.4%
31966
13.1%
41109
 
7.4%
5595
 
4.0%
6558
 
3.7%
7281
 
1.9%
8309
 
2.1%
9184
 
1.2%
ValueCountFrequency (%)
2552
 
< 0.1%
2381
 
< 0.1%
2123
 
< 0.1%
1802
 
< 0.1%
1681
 
< 0.1%
1561
 
< 0.1%
1471
 
< 0.1%
1444
< 0.1%
1351
 
< 0.1%
1328
0.1%

Breed1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.2725939
Minimum0
Maximum307
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:01.546393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile109
Q1265
median266
Q3307
95-th percentile307
Maximum307
Range307
Interquartile range (IQR)42

Descriptive statistics

Standard deviation60.05681836
Coefficient of variation (CV)0.226396619
Kurtosis4.841954513
Mean265.2725939
Median Absolute Deviation (MAD)41
Skewness-2.220919802
Sum3977232
Variance3606.821432
MonotonicityNot monotonic
2022-01-24T20:51:01.650323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3075927
39.5%
2663634
24.2%
2651258
 
8.4%
299342
 
2.3%
264296
 
2.0%
292264
 
1.8%
285221
 
1.5%
141205
 
1.4%
205190
 
1.3%
179167
 
1.1%
Other values (166)2489
16.6%
ValueCountFrequency (%)
05
< 0.1%
12
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
103
 
< 0.1%
112
 
< 0.1%
159
0.1%
161
 
< 0.1%
176
< 0.1%
ValueCountFrequency (%)
3075927
39.5%
30656
 
0.4%
3058
 
0.1%
3047
 
< 0.1%
30342
 
0.3%
3021
 
< 0.1%
3015
 
< 0.1%
30021
 
0.1%
299342
 
2.3%
2981
 
< 0.1%

Breed2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct135
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.00973788
Minimum0
Maximum307
Zeros10762
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:01.761309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3179
95-th percentile307
Maximum307
Range307
Interquartile range (IQR)179

Descriptive statistics

Standard deviation123.0115749
Coefficient of variation (CV)1.662099859
Kurtosis-0.6045826036
Mean74.00973788
Median Absolute Deviation (MAD)0
Skewness1.137499675
Sum1109628
Variance15131.84756
MonotonicityNot monotonic
2022-01-24T20:51:01.867350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010762
71.8%
3071727
 
11.5%
266599
 
4.0%
265321
 
2.1%
299138
 
0.9%
264125
 
0.8%
292105
 
0.7%
21891
 
0.6%
14186
 
0.6%
28578
 
0.5%
Other values (125)961
 
6.4%
ValueCountFrequency (%)
010762
71.8%
11
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
102
 
< 0.1%
142
 
< 0.1%
162
 
< 0.1%
171
 
< 0.1%
183
 
< 0.1%
ValueCountFrequency (%)
3071727
11.5%
30624
 
0.2%
3056
 
< 0.1%
3041
 
< 0.1%
30324
 
0.2%
3022
 
< 0.1%
3011
 
< 0.1%
3009
 
0.1%
299138
 
0.9%
2963
 
< 0.1%

Gender
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
7277 
1
5536 
3
2180 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
27277
48.5%
15536
36.9%
32180
 
14.5%

Length

2022-01-24T20:51:01.965378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:02.022393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
27277
48.5%
15536
36.9%
32180
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Color1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.234175949
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:02.074396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.745225386
Coefficient of variation (CV)0.7811494826
Kurtosis1.006742645
Mean2.234175949
Median Absolute Deviation (MAD)1
Skewness1.472605551
Sum33497
Variance3.045811649
MonotonicityNot monotonic
2022-01-24T20:51:02.143411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
17427
49.5%
23750
25.0%
3947
 
6.3%
5884
 
5.9%
6684
 
4.6%
7667
 
4.4%
4634
 
4.2%
ValueCountFrequency (%)
17427
49.5%
23750
25.0%
3947
 
6.3%
4634
 
4.2%
5884
 
5.9%
6684
 
4.6%
7667
 
4.4%
ValueCountFrequency (%)
7667
 
4.4%
6684
 
4.6%
5884
 
5.9%
4634
 
4.2%
3947
 
6.3%
23750
25.0%
17427
49.5%

Color2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.222837324
Minimum0
Maximum7
Zeros4471
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:02.218421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.742561742
Coefficient of variation (CV)0.8509774049
Kurtosis-1.509334225
Mean3.222837324
Median Absolute Deviation (MAD)2
Skewness0.1909521708
Sum48320
Variance7.521644911
MonotonicityNot monotonic
2022-01-24T20:51:02.289436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04471
29.8%
73438
22.9%
23313
22.1%
51128
 
7.5%
61063
 
7.1%
4870
 
5.8%
3710
 
4.7%
ValueCountFrequency (%)
04471
29.8%
23313
22.1%
3710
 
4.7%
4870
 
5.8%
51128
 
7.5%
61063
 
7.1%
73438
22.9%
ValueCountFrequency (%)
73438
22.9%
61063
 
7.1%
51128
 
7.5%
4870
 
5.8%
3710
 
4.7%
23313
22.1%
04471
29.8%

Color3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.882011605
Minimum0
Maximum7
Zeros10604
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:02.366350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.984085752
Coefficient of variation (CV)1.585583077
Kurtosis-0.9006646498
Mean1.882011605
Median Absolute Deviation (MAD)0
Skewness1.009939016
Sum28217
Variance8.904767778
MonotonicityNot monotonic
2022-01-24T20:51:02.436366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
010604
70.7%
73221
 
21.5%
5417
 
2.8%
6378
 
2.5%
4198
 
1.3%
3175
 
1.2%
ValueCountFrequency (%)
010604
70.7%
3175
 
1.2%
4198
 
1.3%
5417
 
2.8%
6378
 
2.5%
73221
 
21.5%
ValueCountFrequency (%)
73221
 
21.5%
6378
 
2.5%
5417
 
2.8%
4198
 
1.3%
3175
 
1.2%
010604
70.7%

MaturitySize
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
10305 
1
3395 
3
1260 
4
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
210305
68.7%
13395
 
22.6%
31260
 
8.4%
433
 
0.2%

Length

2022-01-24T20:51:02.527386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:02.589403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
210305
68.7%
13395
 
22.6%
31260
 
8.4%
433
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FurLength
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
8808 
2
5361 
3
 
824

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18808
58.7%
25361
35.8%
3824
 
5.5%

Length

2022-01-24T20:51:02.657415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:02.719429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
18808
58.7%
25361
35.8%
3824
 
5.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vaccinated
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
7227 
1
5898 
3
1868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
27227
48.2%
15898
39.3%
31868
 
12.5%

Length

2022-01-24T20:51:02.781358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:02.842371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
27227
48.2%
15898
39.3%
31868
 
12.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Dewormed
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
8397 
2
4815 
3
1781 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
18397
56.0%
24815
32.1%
31781
 
11.9%

Length

2022-01-24T20:51:02.905385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:02.965399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
18397
56.0%
24815
32.1%
31781
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sterilized
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
2
10077 
1
3101 
3
1815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
210077
67.2%
13101
 
20.7%
31815
 
12.1%

Length

2022-01-24T20:51:03.029057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:03.088070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
210077
67.2%
13101
 
20.7%
31815
 
12.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Health
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
1
14478 
2
 
481
3
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
114478
96.6%
2481
 
3.2%
334
 
0.2%

Length

2022-01-24T20:51:03.149099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:03.212099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
114478
96.6%
2481
 
3.2%
334
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Quantity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.576068832
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:03.270112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum20
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.472477255
Coefficient of variation (CV)0.9342721747
Kurtosis34.08677544
Mean1.576068832
Median Absolute Deviation (MAD)0
Skewness4.599819703
Sum23630
Variance2.168189267
MonotonicityNot monotonic
2022-01-24T20:51:03.349130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
111565
77.1%
21422
 
9.5%
3726
 
4.8%
4531
 
3.5%
5333
 
2.2%
6185
 
1.2%
784
 
0.6%
852
 
0.3%
933
 
0.2%
1019
 
0.1%
Other values (9)43
 
0.3%
ValueCountFrequency (%)
111565
77.1%
21422
 
9.5%
3726
 
4.8%
4531
 
3.5%
5333
 
2.2%
6185
 
1.2%
784
 
0.6%
852
 
0.3%
933
 
0.2%
1019
 
0.1%
ValueCountFrequency (%)
2012
0.1%
181
 
< 0.1%
173
 
< 0.1%
163
 
< 0.1%
154
 
< 0.1%
142
 
< 0.1%
132
 
< 0.1%
126
 
< 0.1%
1110
0.1%
1019
0.1%

Fee
Real number (ℝ≥0)

ZEROS

Distinct74
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.25998799
Minimum0
Maximum3000
Zeros12663
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:03.456154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile150
Maximum3000
Range3000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation78.41454767
Coefficient of variation (CV)3.688362745
Kurtosis191.7006408
Mean21.25998799
Median Absolute Deviation (MAD)0
Skewness8.921384879
Sum318751
Variance6148.841286
MonotonicityNot monotonic
2022-01-24T20:51:03.567178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012663
84.5%
50468
 
3.1%
100408
 
2.7%
200219
 
1.5%
150162
 
1.1%
20136
 
0.9%
300120
 
0.8%
30103
 
0.7%
25092
 
0.6%
182
 
0.5%
Other values (64)540
 
3.6%
ValueCountFrequency (%)
012663
84.5%
182
 
0.5%
21
 
< 0.1%
524
 
0.2%
87
 
< 0.1%
95
 
< 0.1%
1070
 
0.5%
141
 
< 0.1%
1520
 
0.1%
20136
 
0.9%
ValueCountFrequency (%)
30001
 
< 0.1%
20001
 
< 0.1%
10004
 
< 0.1%
8002
 
< 0.1%
7507
< 0.1%
7005
 
< 0.1%
6881
 
< 0.1%
6504
 
< 0.1%
60013
0.1%
5991
 
< 0.1%

State
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41346.02835
Minimum41324
Maximum41415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:03.668205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum41324
5-th percentile41326
Q141326
median41326
Q341401
95-th percentile41401
Maximum41415
Range91
Interquartile range (IQR)75

Descriptive statistics

Standard deviation32.44415298
Coefficient of variation (CV)0.0007846981748
Kurtosis-0.7847166994
Mean41346.02835
Median Absolute Deviation (MAD)0
Skewness1.091114825
Sum619901003
Variance1052.623062
MonotonicityNot monotonic
2022-01-24T20:51:03.756222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
413268714
58.1%
414013845
25.6%
41327843
 
5.6%
41336507
 
3.4%
41330420
 
2.8%
41332253
 
1.7%
41324137
 
0.9%
41325110
 
0.7%
4133585
 
0.6%
4136126
 
0.2%
Other values (4)53
 
0.4%
ValueCountFrequency (%)
41324137
 
0.9%
41325110
 
0.7%
413268714
58.1%
41327843
 
5.6%
41330420
 
2.8%
41332253
 
1.7%
4133585
 
0.6%
41336507
 
3.4%
4134213
 
0.1%
4134522
 
0.1%
ValueCountFrequency (%)
414153
 
< 0.1%
414013845
25.6%
4136715
 
0.1%
4136126
 
0.2%
4134522
 
0.1%
4134213
 
0.1%
41336507
 
3.4%
4133585
 
0.6%
41332253
 
1.7%
41330420
 
2.8%

RescuerID
Categorical

HIGH CARDINALITY

Distinct5595
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
fa90fa5b1ee11c86938398b60abc32cb
 
459
aa66486163b6cbc25ea62a34b11c9b91
 
315
c00756f2bdd8fa88fc9f07a8309f7d5d
 
231
b53c34474d9e24574bcec6a3d3306a0d
 
228
ee2747ce26468ec44c7194e7d1d9dad9
 
156
Other values (5590)
13604 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3783 ?
Unique (%)25.2%

Sample

1st row8480853f516546f6cf33aa88cd76c379
2nd row3082c7125d8fb66f7dd4bff4192c8b14
3rd rowfa90fa5b1ee11c86938398b60abc32cb
4th row9238e4f44c71a75282e62f7136c6b240
5th row95481e953f8aed9ec3d16fc4509537e8

Common Values

ValueCountFrequency (%)
fa90fa5b1ee11c86938398b60abc32cb459
 
3.1%
aa66486163b6cbc25ea62a34b11c9b91315
 
2.1%
c00756f2bdd8fa88fc9f07a8309f7d5d231
 
1.5%
b53c34474d9e24574bcec6a3d3306a0d228
 
1.5%
ee2747ce26468ec44c7194e7d1d9dad9156
 
1.0%
95481e953f8aed9ec3d16fc4509537e8134
 
0.9%
b770bac0ca797cf1433c48a35d30c4cb111
 
0.7%
a042471e0f43f2cf707104a1a138a7df95
 
0.6%
fd970cc91d06d82eebf046340137b27293
 
0.6%
7ed6d84e2e6879245e55447aee39c32885
 
0.6%
Other values (5585)13086
87.3%

Length

2022-01-24T20:51:03.851244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fa90fa5b1ee11c86938398b60abc32cb459
 
3.1%
aa66486163b6cbc25ea62a34b11c9b91315
 
2.1%
c00756f2bdd8fa88fc9f07a8309f7d5d231
 
1.5%
b53c34474d9e24574bcec6a3d3306a0d228
 
1.5%
ee2747ce26468ec44c7194e7d1d9dad9156
 
1.0%
95481e953f8aed9ec3d16fc4509537e8134
 
0.9%
b770bac0ca797cf1433c48a35d30c4cb111
 
0.7%
a042471e0f43f2cf707104a1a138a7df95
 
0.6%
fd970cc91d06d82eebf046340137b27293
 
0.6%
7ed6d84e2e6879245e55447aee39c32885
 
0.6%
Other values (5585)13086
87.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VideoAmt
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05675982125
Minimum0
Maximum8
Zeros14419
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:03.933261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3461845502
Coefficient of variation (CV)6.099112763
Kurtosis124.4246316
Mean0.05675982125
Median Absolute Deviation (MAD)0
Skewness9.45853321
Sum851
Variance0.1198437428
MonotonicityNot monotonic
2022-01-24T20:51:04.013331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
014419
96.2%
1417
 
2.8%
292
 
0.6%
336
 
0.2%
415
 
0.1%
57
 
< 0.1%
64
 
< 0.1%
82
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
014419
96.2%
1417
 
2.8%
292
 
0.6%
336
 
0.2%
415
 
0.1%
57
 
< 0.1%
64
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
71
 
< 0.1%
64
 
< 0.1%
57
 
< 0.1%
415
 
0.1%
336
 
0.2%
292
 
0.6%
1417
 
2.8%
014419
96.2%

Description
Categorical

HIGH CARDINALITY

Distinct14032
Distinct (%)93.7%
Missing12
Missing (%)0.1%
Memory size117.3 KiB
For Adoption
 
164
Dog 4 Adoption
 
54
Cat for adoption
 
25
Friendly
 
20
Dog for adoption
 
18
Other values (14027)
14700 

Length

Max length6664
Median length238
Mean length339.5851412
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13710 ?
Unique (%)91.5%

Sample

1st rowNibble is a 3+ month old ball of cuteness. He is energetic and playful. I rescued a couple of cats a few months ago but could not get them neutered in time as the clinic was fully scheduled. The result was this little kitty. I do not have enough space and funds to care for more cats in my household. Looking for responsible people to take over Nibble's care.
2nd rowI just found it alone yesterday near my apartment. It was shaking so I had to bring it home to provide temporary care.
3rd rowTheir pregnant mother was dumped by her irresponsible owner at the roadside near some shops in Subang Jaya. Gave birth to them at the roadside. They are all healthy and adorable puppies. Already dewormed, vaccinated and ready to go to a home. No tying or caging for long hours as guard dogs. However, it is acceptable to cage or tie for precautionary purposes. Interested to adopt pls call me.
4th rowGood guard dog, very alert, active, obedience waiting for her good master, plz call or sms for more details if you really get interested, thanks!!
5th rowThis handsome yet cute boy is up for adoption. He is the most playful pal we've seen in our puppies. He loves to nibble on shoelaces , Chase you at such a young age. Imagine what a cute brat he will be when he grows. We are looking for a loving home for Hunter , one that will take care of him and give him the love that he needs. Please call urgently if you would like to adopt this cutie.

Common Values

ValueCountFrequency (%)
For Adoption164
 
1.1%
Dog 4 Adoption54
 
0.4%
Cat for adoption25
 
0.2%
Friendly20
 
0.1%
Dog for adoption18
 
0.1%
Please feel free to contact us : Stuart18
 
0.1%
I need a new home!! Contact Furry Friends Farm if you want to adopt me.15
 
0.1%
PLEASE RESCUE/ADOPT ME FROM KLANG POUND OR I WILL BE PUT TO DEATH BY THIS WEEK, 28/3/10. I don't want to die,and I will love you immensely for saving me. Help!!! Please call ----------------------------------------------------- Adoption Procedure: This dog has been caught by Majlis Perbandaran Klang, and if nobody comes forward to adopt it, it will be euthanized within a few days. Even owned dogs are also often caught, and the owners are not aware for it. Those wishing to adopt this pet from Klang Dog Pound, please follow the procedures below: 1. Drive to Pusat Kurungan Haiwan Lebuh Sultan Muhammad Kawasan Perindustrian Bandar Sultan Sulaiman Pelabuhan Klang Tel : (For Sat & Sun, opening hours are 8am - 12pm) 2. Secure a Borang Permohonan Tuntutan Anjing, Selepas Tempoh 7 hari. Complete it & ensure it is endorsed by the relevant officier & stamped with relevant chop. 3. Provide a photostated copy of your Identification Card or Passport with each application * policies & requirements stiffen day by day * Advisable to provide a copy of IC/Passport per application (Just in case) * Secure extra application if there is any inkling of additional adoption. * Don't expect any leniency (Even we committee members, slaves & beggars don't have any unless OK by big guy) 4. Please be compassionate. Put yourself in their shoes: locked inside knowing its over. THEY DO KNOW. 5. I have seen them wasted much close to D days. Don't tell me they didn't undergo heightened enxiety & despair in anticipation of the end. What's worse their owners never came for them. Directions to Klang Dog Pound ================================ 1) Use Kesas Highway 2) Head for North Port till you see the signboard that writes "Melbourne 14 Days", then turn Right 3) Keep Left and turn Left at traffic light 4) Stay beside flyover and turn Right at immediate traffic light 5) Drive towards Sultan Sulaiman Industrial Estate 6) Go up first set of flyover 7) Keep Left till you see Pusat Kurungan Haiwan signboard 8) Turn Left 9) Drive on till you see gravel road work beside retention pond at the right 10) Turn in and turn Right till you reach a blue-roofed pound15
 
0.1%
The lil' puppy is currently taking shelter at SPCA Seberang Perai. Those interested to adopt her may contact us via email.14
 
0.1%
The puppy is currently taking shelter at SPCA Seberang Perai. Please contact SPCA Seberang Perai if you are interested to adopt her as your pet.13
 
0.1%
Other values (14022)14625
97.5%

Length

2022-01-24T20:51:04.134360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and31297
 
3.3%
to29340
 
3.1%
a24359
 
2.6%
the18661
 
2.0%
is18362
 
1.9%
for14731
 
1.6%
13581
 
1.4%
i11131
 
1.2%
her10753
 
1.1%
she10231
 
1.1%
Other values (28145)761418
80.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PetID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct14993
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
86e1089a3
 
1
aa91c3400
 
1
a0f76e19b
 
1
e488a36cf
 
1
2d5eababe
 
1
Other values (14988)
14988 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14993 ?
Unique (%)100.0%

Sample

1st row86e1089a3
2nd row6296e909a
3rd row3422e4906
4th row5842f1ff5
5th row850a43f90

Common Values

ValueCountFrequency (%)
86e1089a31
 
< 0.1%
aa91c34001
 
< 0.1%
a0f76e19b1
 
< 0.1%
e488a36cf1
 
< 0.1%
2d5eababe1
 
< 0.1%
edb154f1c1
 
< 0.1%
9882871d91
 
< 0.1%
8ea9f12281
 
< 0.1%
1df948d0e1
 
< 0.1%
c37419ab71
 
< 0.1%
Other values (14983)14983
99.9%

Length

2022-01-24T20:51:04.238384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
86e1089a31
 
< 0.1%
aaedd873d1
 
< 0.1%
7843a9dca1
 
< 0.1%
efbf1703a1
 
< 0.1%
3422e49061
 
< 0.1%
5842f1ff51
 
< 0.1%
850a43f901
 
< 0.1%
d24c30b4b1
 
< 0.1%
1caa6fcdb1
 
< 0.1%
97aa9eeac1
 
< 0.1%
Other values (14983)14983
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PhotoAmt
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.889214967
Minimum0
Maximum30
Zeros341
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2022-01-24T20:51:04.325401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.487810245
Coefficient of variation (CV)0.896790297
Kurtosis12.64591839
Mean3.889214967
Median Absolute Deviation (MAD)2
Skewness2.860638032
Sum58311
Variance12.16482031
MonotonicityNot monotonic
2022-01-24T20:51:04.417424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13075
20.5%
22518
16.8%
32511
16.7%
52147
14.3%
41881
12.5%
6621
 
4.1%
7432
 
2.9%
0341
 
2.3%
8314
 
2.1%
9231
 
1.5%
Other values (21)922
 
6.1%
ValueCountFrequency (%)
0341
 
2.3%
13075
20.5%
22518
16.8%
32511
16.7%
41881
12.5%
52147
14.3%
6621
 
4.1%
7432
 
2.9%
8314
 
2.1%
9231
 
1.5%
ValueCountFrequency (%)
3019
0.1%
296
 
< 0.1%
287
 
< 0.1%
276
 
< 0.1%
2610
0.1%
258
0.1%
2415
0.1%
2312
0.1%
229
0.1%
2116
0.1%

AdoptionSpeed
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
4
4197 
2
4037 
3
3259 
1
3090 
0
 
410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
44197
28.0%
24037
26.9%
33259
21.7%
13090
20.6%
0410
 
2.7%

Length

2022-01-24T20:51:04.513444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-24T20:51:04.576459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
44197
28.0%
24037
26.9%
33259
21.7%
13090
20.6%
0410
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-24T20:50:58.824903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-01-24T20:50:57.578387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-24T20:50:58.721889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-24T20:51:04.678481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-24T20:51:04.872584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-24T20:51:05.333967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-24T20:51:05.527564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-24T20:51:05.682619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-24T20:51:00.049907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-24T20:51:00.411951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-24T20:51:00.608159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-24T20:51:00.708180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

TypeNameAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateRescuerIDVideoAmtDescriptionPetIDPhotoAmtAdoptionSpeed
02Nibble3299011701122211100413268480853f516546f6cf33aa88cd76c3790Nibble is a 3+ month old ball of cuteness. He is energetic and playful. I rescued a couple of cats a few months ago but could not get them neutered in time as the clinic was fully scheduled. The result was this little kitty. I do not have enough space and funds to care for more cats in my household. Looking for responsible people to take over Nibble's care.86e1089a31.02
12No Name Yet12650112022333110414013082c7125d8fb66f7dd4bff4192c8b140I just found it alone yesterday near my apartment. It was shaking so I had to bring it home to provide temporary care.6296e909a2.00
21Brisco1307012702211211041326fa90fa5b1ee11c86938398b60abc32cb0Their pregnant mother was dumped by her irresponsible owner at the roadside near some shops in Subang Jaya. Gave birth to them at the roadside. They are all healthy and adorable puppies. Already dewormed, vaccinated and ready to go to a home. No tying or caging for long hours as guard dogs. However, it is acceptable to cage or tie for precautionary purposes. Interested to adopt pls call me.3422e49067.03
31Miko4307021202111211150414019238e4f44c71a75282e62f7136c6b2400Good guard dog, very alert, active, obedience waiting for her good master, plz call or sms for more details if you really get interested, thanks!!5842f1ff58.02
41Hunter130701100212221104132695481e953f8aed9ec3d16fc4509537e80This handsome yet cute boy is up for adoption. He is the most playful pal we've seen in our puppies. He loves to nibble on shoelaces , Chase you at such a young age. Imagine what a cute brat he will be when he grows. We are looking for a loving home for Hunter , one that will take care of him and give him the love that he needs. Please call urgently if you would like to adopt this cutie.850a43f903.02
52NaN326602560212221104132622fe332bf9c924d4718005891c63fbed0This is a stray kitten that came to my house. Have been feeding it, but cannot keep it.d24c30b4b2.02
62BULAT1226426411002322311300413261e0b5a458b5b77f5af581d57ebf570b30anyone within the area of ipoh or taiping who interested to adopt my cat can contact my father at this number (mazuvil)or can just email me. currently bulat is at my hometown at perak but anyone outside the area still want to adopt can travel there to my hometown.there is a lot of cats in my house rite now..i think i should let one of them go to a better owner who can give better attention to him.1caa6fcdb3.01
71Siu Pak & Her 6 Puppies03070212721222160413261fba5f6e5480946254590d48f9c5198d0Siu Pak just give birth on 13/6/10 to 6puppies. Interested pls call or sms me. Left 2female puppies on 2/7/1097aa9eeac9.03
82NaN2265026002222211041326d8af7afece71334473575c9f70daf00d0healthy and active, feisty kitten found in neighbours' garden. Not sure of sex.c06d167ca6.01
92Kitty122650217022333110413261f3f36e4b18e94855b3e88af0852fdc40Very manja and gentle stray cat found, we would really like to find a home for it because we cannot keep her for ourselves for long. Has a very cute high pitch but soft meow. Please contact me if you would be interested in adopting.7a0942d612.04

Last rows

TypeNameAgeBreed1Breed2GenderColor1Color2Color3MaturitySizeFurLengthVaccinatedDewormedSterilizedHealthQuantityFeeStateRescuerIDVideoAmtDescriptionPetIDPhotoAmtAdoptionSpeed
149831Alger3307011272211211041326fa90fa5b1ee11c86938398b60abc32cb0He is very intelligent and cute. Fluffy and looks much better in real life than in the photo. He deserves a good home. No tying or caging for long hours except for precautionary purposes Serious adopter pls callcca88204d7.02
149841NaN60307022502233311041324c8ea0bc42e630c72747986c4c0ce36aa0abandoned,but healthyf5dc70d351.04
149851Terry2417930712372233211041326719987dce7aeb027fdfa91b4808001990been at my place for a while..am hoping to find it a good homee7f7066b60.04
149862Pets + Strays : BlueEyed BlackWhite126602567212121104140190569c3f7cb0af35cba5dac82c0ac9d701 month old white + grey kitten for adoption near HUKM, KL, near Bdr Tun Razak Gender / medical record + costs To Be Confirmed. Adopter MUST commit to NEUTER kitten when it is : * 4-6 months old * on heat whichever comes first, provided that it is * 1.4 kg weight MINIMUM Whatsapp for adopption / FREE gift / startup kit / Sign up contract to buy supplies from Pets + Strays, comes with FREE gifts whilst stocks last36e7f8d831.03
149871Snowy619502170131121104140179309f4027f2fedb4349a298c69fe56f0ooooo4d163b7311.00
149882NaN226603100222221404132661c84bd7bcb6fb31d2d480b1bcf9682e0I have 4 kittens that need to be adopt urgently. It about 1 1/2 months old. My cat got pregnant before we got the chance to get its muted. The kittens are healthy and are eating kittens biscuits now. They are very playful and love being pat I prefer the kittens to be going to the same home but I do understands and its can be adopt separately. I'm hopping the kittens will get a lovely home soondc0935a843.02
149892Serato & Eddie60265264314722111120413261d5096c4a5e159a3b750c5cfcf6ceabf0Serato(female cat- 3 color) is 4 years old and Eddie(male cat- white and cream) is 1 years plus. Both are toilet train and can't be separated. Needs a loving home together.a01ab5b303.04
149902Monkies22652663567322131530413266f40a7acfad5cc0bb3e44591ea446c050Mix breed, good temperament kittens. Love humans. Very friendly.d981b63955.03
149912Ms Daym9266024701111111041336c311c0c569245baa147d91fa4e351ae40she is very shy..adventures and independent..she just hates cages..but loves climbing trees and rooftops..however she is very loving.e4da1c9e43.04
149921Fili1307307120021222110413329ed1d5493d223eaa5024c1a031dbc9c20Fili just loves laying around and also loves being under the sun; Very laidback and quiet.a83d95ead1.03